RESUMEN
BACKGROUND: Alzheimer's disease has been known for more than 100 years and the underlying molecular mechanisms are not yet completely understood. The identification of genes involved in the processes in Alzheimer affected brain is an important step towards such an understanding. Genes differentially expressed in diseased and healthy brains are promising candidates. RESULTS: Based on microarray data we identify potential biomarkers as well as biomarker combinations using three feature selection methods: information gain, mean decrease accuracy of random forest and a wrapper of genetic algorithm and support vector machine (GA/SVM). Information gain and random forest are two commonly used methods. We compare their output to the results obtained from GA/SVM. GA/SVM is rarely used for the analysis of microarray data, but it is able to identify genes capable of classifying tissues into different classes at least as well as the two reference methods. CONCLUSION: Compared to the other methods, GA/SVM has the advantage of finding small, less redundant sets of genes that, in combination, show superior classification characteristics. The biological significance of the genes and gene pairs is discussed.
Asunto(s)
Enfermedad de Alzheimer/genética , Inteligencia Artificial/estadística & datos numéricos , Perfilación de la Expresión Génica/estadística & datos numéricos , Análisis de Matrices Tisulares/estadística & datos numéricos , Expresión Génica , Marcadores Genéticos , Humanos , Análisis de Secuencia por Matrices de Oligonucleótidos/estadística & datos numéricos , Máquina de Vectores de SoporteRESUMEN
Pluripotent stem cells are able to self-renew and to differentiate into all adult cell types. Many studies report data describing these cells and characterize them in molecular terms. Gene expression data of pluripotent and non-pluripotent cells from mouse were assembled. Machine learning was applied to classify samples into pluripotent and non-pluripotent cells. To identify minimal sets of best biomarkers, three methods were used: information gain, random forests, and genetic algorithm.
Asunto(s)
Inteligencia Artificial , Minería de Datos/métodos , Bases de Datos Genéticas , Perfilación de la Expresión Génica/métodos , Células Madre Pluripotentes/clasificación , Células Madre Pluripotentes/metabolismo , Proteoma/análisis , Animales , Biomarcadores/análisis , Sistemas de Administración de Bases de Datos , RatonesRESUMEN
Pluripotent stem cells are able to self-renew, and to differentiate into all adult cell types. Many studies report data describing these cells, and characterize them in molecular terms. Machine learning yields classifiers that can accurately identify pluripotent stem cells, but there is a lack of studies yielding minimal sets of best biomarkers (genes/features). We assembled gene expression data of pluripotent stem cells and non-pluripotent cells from the mouse. After normalization and filtering, we applied machine learning, classifying samples into pluripotent and non-pluripotent with high cross-validated accuracy. Furthermore, to identify minimal sets of best biomarkers, we used three methods: information gain, random forests and a wrapper of genetic algorithm and support vector machine (GA/SVM). We demonstrate that the GA/SVM biomarkers work best in combination with each other; pathway and enrichment analyses show that they cover the widest variety of processes implicated in pluripotency. The GA/SVM wrapper yields best biomarkers, no matter which classification method is used. The consensus best biomarker based on the three methods is Tet1, implicated in pluripotency just recently. The best biomarker based on the GA/SVM wrapper approach alone is Fam134b, possibly a missing link between pluripotency and some standard surface markers of unknown function processed by the Golgi apparatus.